arXiv:2509.00303v3 Announce Type: replace-cross Abstract: In this work, we present the \texttt{LLM ORDER BY} semantic operator as a logical abstraction and conduct a systematic study of its physical implementations. First, we propose several improvements to existing semantic sorting algorithms and introduce a semantic-aware external merge sort algorithm. Our extensive evaluation reveals that no single implementation offers universal optimality on all datasets. From our evaluations, we observe a general test-time scaling relationship between sorting cost and the ordering quality for comparison-
The rapid advancement and integration of large language models into various applications necessitate optimized data handling and ordering mechanisms to improve efficiency and performance.
This work directly addresses a critical performance bottleneck for large language models, impacting the efficiency and scalability of AI systems across industries by improving data processing.
New methodologies for semantic ordering with LLMs will enable more efficient data retrieval and processing, leading to faster and more accurate AI-driven applications.
- · AI software developers
- · Cloud providers
- · Data analytics companies
- · LLM application users
- · Inefficient legacy data systems
- · Companies without AI integration strategies
Improved performance and responsiveness of applications utilizing large language models.
Accelerated development and deployment of sophisticated AI agents due to better data management capabilities.
Enhanced user experience and broader adoption of AI tools as performance barriers are reduced.
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Read at arXiv cs.AI